########################### # UI for Meeting RAG Q&A. # ########################### ##################### Imports ##################### import gradio as gr from utilities.setup import get_files #from server import EmbeddingService, QAService from connections.pincone import PineconeConnector from services.embed_service.embed import EmbeddingService from server import QAService #import spaces #################### Functions #################### def process_transcripts(files, context): print(files) with EmbeddingService(conf, pinecone=pinecones) as e: f = e.run(files) # some way to wait or a progress bar? return "Completed Loading Data" #@spaces.GPU def retrieve_answer(question, context): #with QAService(conf) as q: # q.run(question) return question + context ##################### Process ##################### def main(conf): with gr.Blocks() as demo: # Main page with gr.TabItem(conf["layout"]["page_names"][0]): gr.Markdown(get_files.load_markdown_file(conf["layout"]["about"])) # User config page with gr.TabItem(conf["layout"]["page_names"][1]): gr.Markdown("# Upload Transcript and Necessary Context") gr.Markdown("Please wait as the transcript is being processed.") load_file = gr.UploadButton(label="Upload Transcript (.vtt)", file_types=[".vtt"], file_count='multiple') goals = gr.Textbox(label="Goals for the Meeting", value=conf["defaults"]["goals"]) # not incorporated yet. Will be with Q&A. repository = gr.Textbox(label="Progress", value="Waiting for load...", visible=True) load_file.upload(process_transcripts, [load_file, goals], repository) # Meeting Question & Answer Page with gr.TabItem(conf["layout"]["page_names"][2]): question = gr.Textbox(label="Ask a Question", value=conf["defaults"]["question"]) ask_button = gr.Button("Ask!") model_output = gr.components.Textbox(label="Answer") ask_button.click(fn=retrieve_answer, inputs=[question, goals], outputs=model_output) demo.launch() ##################### Execute ##################### if __name__ == "__main__": # Get config conf = get_files.json_cfg() # Get keys keys = get_files.get_keys() # initialize pinecone connector pc_connector = PineconeConnector( api_key=keys["pinecone"], index_name=conf["embeddings"]["index_name"], embedding=conf["embeddings"]["embedding"], ) pinecones = pc_connector.run() # initialize model connector # run main main(conf)